By the end of this course, students will be able to apply computational algorithms in statistics, including numerical optimization, Monte Carlo simulation, randomization techniques, and graphical methods; implement statistical methods using programming tools such as R, MATLAB, Python, or C to solve complex data analysis problems; evaluate algorithm performance in terms of efficiency, accuracy, and suitability for different types of datasets; analyze and interpret results from computational procedures, drawing valid statistical conclusions; communicate findings effectively, both in written reports and oral presentations, for research or applied data-driven contexts; design and conduct reproducible computational experiments for applied statistical problems; and lastly, critically compare and select computational approaches, considering trade-offs between theoretical rigor, computational cost, and practical applicability.